In order to achieve faster and more robust convergence (particularly under noisy working environments), a sliding-mode-theory-based learning algorithm has been proposed to tune both the premise and consequent parts of type-2 fuzzy neural networks (FNNs) in this paper. Different from recent studies, where sliding-mode-control-theory-based rules are proposed for only the consequent part of the network, the developed algorithm applies fully-sliding-mode parameter update rules for both the premise and consequent parts of type-2 FNNs. In addition, the responsible parameter for sharing the contributions of the lower and upper parts of the type-2 fuzzy membership functions is also tuned. Moreover, the learning rate of the network is updated during the online training. The stability of the proposed learning algorithm has been proved by using an appropriate Lyapunov function. Several comparisons have been realized and shown that the proposed algorithm has faster convergence speed than the existing methods such as gradient-based and swarm-intelligence-based methods. Moreover, the proposed learning algorithm has a closed form, and it is easier to implement than the other existing methods.
- Sliding-mode learning algorithm
- system identification
- type-2 fuzzy logic systems (FLSs)
- type-2 fuzzy neural networks (FNNs)